When are time series predictions causal? The potential system and dynamic causal effects
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| Format: | Preprint |
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2026
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| _version_ | 1866917355389452288 |
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| author | Carlson, Jacob Shephard, Neil |
| author_facet | Carlson, Jacob Shephard, Neil |
| contents | The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions and SVARs. It closes a gap between time series causality and nonparametric cross-sectional causal methods, and provides a foundation for many new methods which have causal content. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_20394 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When are time series predictions causal? The potential system and dynamic causal effects Carlson, Jacob Shephard, Neil Econometrics Methodology The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions and SVARs. It closes a gap between time series causality and nonparametric cross-sectional causal methods, and provides a foundation for many new methods which have causal content. |
| title | When are time series predictions causal? The potential system and dynamic causal effects |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2603.20394 |